Whole genome association (WGA) in the Baltimore Longitudinal Study of Aging and the InCHIANTI cohorts is a cost-effective and statistically powerful method for identifying genetic variations of importance in aging phenotypes. Preliminary data show that quantitative traits for aging can be used to identify important gene variants. This proposal builds on recent successes and supplements NIH disease-based WGA study programs. The proposed study design will minimize false positive associations by building in replication and by creating a strong collaboration with other NIA- and NIH-funded studies that either have perfomed GWAS or have specific phenotypes and DNA available. This new resource has enabled the BLSA and InCHIANTI studies to participate in many collaborative projects in the United States and Europe aimed at understanding the contribution of genetic variability to aging-relevant phenotypes. There are over 10 million common variants in the human genome, a small proportion of which may have significant effects on biological pathways, disease and aging. With falling costs, it is now cost effective to genotype 550K carefully chosen markers across the genome, effectively identifying over 90% of all common variations. Imputation strategies using population specific references has also allowed generation of up to 38 million SNPs allowing better coverage of loci not represented on the genotyping chips. These markers can then be linked statistically to relevant phenotypes to perform genome wide or whole genome association studies (WGA). The particular focus is to identify genes or patterns of genes whose variation is associated with differences in functional status and quality of life with aging. The aging process is governed by a range of biological pathways which have diverse effects across body systems, not limited to specific diseases. Identifying exactly which gene variants are associated with early onset, rates or aspects of underlying aging phenotypes would provide vital insights into the aging process in humans and open up new areas for prevention, treatment and prognostic testing. The NIH has invested substantial resources in very high quality measurement of aging phenotypes in the BLSA and InCHIANTI aging studies. Both BLSA and InCHIANTI have comparable measures of key phenotypes, including: insulin resistance, muscle strength, gait speed, bone density, testosterone decline and other quantitative traits of aging including cognition and hearing loss, etc. These phenotypes have clear clinical and public health importance and have been shown to be effective in identifying genetic variations. Both studies have information on intermediate markers or endo-phenotypes on critical pathways, including a large set of inflammatory markers. Crucially, these cohorts have several waves of observations, allowing more accurate staging of biological age across waves, and calculation of rates of decline. By examining longitudinal trajectories of biological variables and by using the age-specific incidence of critical diseases (cardiovascular diseases, dementia, fractures etc.) as outcomes, we can begin to understand the nature of genetic propensity to disease risk and study factors detectable in early life that predict quality of life in old age. In addition, the familial structure of the two cohorts has been well characterized. In the BLSA, such structure will be further enhanced by labeling all the BLSA samples using a standard set of paternity markers. Once genetic variants are identified statistically, follow-up lab work will characterize the biological effects. The BLSA and InCHIANTI study will collect samples to be used in RNA expression studies in follow-up work. Other samples, such as serum, for proteomics may also be collected. These cohorts therefore provide continuing and broad-based resources for discovery, replication, and characterization of the biological effects of active variants. Objectives: 1. Identify polymorphisms associated with defined quantitative aging phenotypes, including circulating proteins, physical performance, cognitive function, muscle strength or sarcopenia, osteoporosis and insulin resistance, taking steps to exclude false positive associations 2. Provide a continuing resource for initial assessment against other measured phenotypes, including eventual outcomes and rates of change measures across several waves of follow-up in these cohorts Specific aims: 1. Undertake whole genome genotyping in about 1200 BLSA participants and 1200 InCHIANTI participants, using the 550K Illumina platform in the NIA Laboratory of Neurogenetics and imputation against different reference genome. 2. Identify all SNPs statistically associated with physical performance, cognitive function and other selected aging phenotypes (both cross-sectional and longitudinal), expecting that several hundred apparent associations will be false positives. 3. Attempt to replicate SNP associations already found in two cohorts in independent NIA-supported study samples, with the expectation that a significant number will fail to replicate and those that are replicated twice will indicate important and true associations. 4. Develop and maintain a bioinformatics resource on this WGA, which will be used for initial study of other measured phenotypes, and also to measure the aging effects of gene variants identified in the literature for specific diseases. 5. Develop statistical genetic expertise within NIA and establish strong collaboration with other groups focusing on the genetic contribution to age-associated traits in other studies.